Abstract
This work aimed to investigate the contribution of each single joint during the execution of whole-body reaching tasks to the overall discomfort of the worker evaluated through standard observational methods.
Forty-five healthy volunteers were asked to reach and rotate 2 spheres placed on a custom-made rack in standardized positions, i.e., above the head and one at floor level at the center side. Whole-body kinematics was acquired via a system based on wearable inertial measurement units, which represent proper enabling technologies within human-centered “Industry 5.0” context. Standard ergonomic scales including RULA (Rapid Upper Limb Assessment), REBA (Rapid Entire Body Assessment), and MMGA (Method for Movement and Gesture Assessment), were assessed for each subject and each sphere position. Moreover, a quantitative index based on actual joint kinematics, i.e., the W1 index, was computed for each joint angle involved in the task. Pearson’s correlation analysis was performed for W1 relative to each joint with respect to RULA, REBA, and MMGA scores.
Considering REBA and MMGA scores, the most comfortable reaching areas were the ones in which the sphere was positioned at the top; in contrast, the lowest positions evidenced the most increased discomfort indexes. The RULA did not result sensitive to the different positions, while REBA and MMGA seemed to be more influenced by the range of motion of the lower limb joint angles than the upper limb ones.
This study underlines the necessity to focus on multiple potential contributors to work-related musculoskeletal disorders and underlines the importance of subject-specific approaches toward risk assessment by exploiting quantitative measurements and wearable technologies.
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Scalona, E., De Marco, D., Avanzini, P., Fabbri Destro, M., Andreoni, G., Lopomo, N.F. (2023). Human Ergonomic Assessment Within “Industry 5.0” Workplace: Do Standard Observational Methods Correlate with Kinematic-Based Index in Reaching Tasks?. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14028. Springer, Cham. https://doi.org/10.1007/978-3-031-35741-1_17
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